Magnetic resonance imaging (MRI) provides a powerful tool for non-invasive imaging for treatment assessment and for minimally invasive surgery. The contrast sensitivity of the MRI provides a capability of non-invasively revealing structures and functions of internal tissues and organs not known to other imaging techniques such as, for example, CT scan or Ultrasound.
Physiological fluctuations are a common source of artifacts and noise in medical imaging. These fluctuations are typically manifested as phase shifts in the collected MR signal and as variations in the resonant frequency. Due to relatively slow collection of image data in phase-encode direction Gradient-Recalled Echo Echo Planar Imaging (GRE-EPI) is especially susceptible to these phenomena. MR phase variations during image collection cause ghosting artifacts in the phase-encode direction of a reconstructed EPI image which possibly overlaps with anatomical regions of interest. A first step in correcting the phase of the MR signal is to collect a reference EPI scan prior to the imaging sequence with no phase-encoding gradient, and to register the phase of subsequent raw image data to that of the reference scan before reconstructing the image data into images as taught in: Schmitt F., Wielopolski P. A., “Echo-planar image reconstruction”, in: Schmitt F., Stehling M. K., Turner R., editors, “Echo-planar imaging: theory, technique, and application”, Berlin, Springer Verlag, 1998, 141–178, herein incorporated by reference. Hence, this technique corrects for phase accrual during the collection of image data. This technique is standard for most EPI applications. However, if imaging planes are located in areas of high magnetic susceptibility causing phase distortions in both the reference scan and the image data, ghosting artifacts and geometric distortions still remain.
In imaging sessions where multiple scans are collected over time, as in functional magnetic resonance imaging (fMRI), phase distortions in the MR signal occur due to physiological fluctuations between images taken such as sudden head motion or changes in respiration. The removal of artifacts resulting from these fluctuations is of particular importance in fMRI since data analysis techniques rely on the variation of image pixel intensity to identify, for example, brain regions involved in a specific task. Image artifacts have placed limitations on numerous studies of brain activation using fMRI. For example, the majority of studies involving language have relied on the mental generation of words rather than speech production due to artifacts that accompany jaw movements and the resonating oral cavity. Some fMRI studies have attempted speech production by designing post-processing strategies to remove motion artifacts as disclosed in: Huang J., Carr T. H., Cao Y, “Comparing cortical activations for silent and overt speech using event-related fMRI”, Hum Brain Mapp., 2002, 15, 39–53, herein incorporated by reference. However, movements occurring outside the imaging field lead to MR phase fluctuations giving rise to magnitude artifacts in the reconstructed images, especially for images taken near face and jaw. Existing motion correction techniques are not capable of correcting these artifacts since rigid motion of the brain is not the result of the artifact. In this case, false positive activations occur or significant brain activity is missed depending on the manifestation of the artifact.
A common technique to correct for time-varying fluctuations in data phase is to use a navigator echo scheme as disclosed in: Hu X., Kim S. G., “Reduction of signal fluctuation in functional MRI using navigator echoes”, Magn. Reson. Med., 1994, 31, 495–503, herein incorporated by reference. A navigator echo is an additional line of k space, i.e. at ky=0, collected after each RF pulse. This echo is used to monitor changes of the phase at the beginning of each image collection due to physiological fluctuations such as respiration. Essentially, the correction is based on registering the phase of the nth image raw data set to the reference phase of the first image using the phase of the nth navigator echo. Although this method improves image quality, ghosting artifacts remain due to inaccuracies in tracking the distortions in phase.
Another method for correcting physiological fluctuations is based on the use of retrospective modeling of the cardiac and/or respiratory cycles using data collected from physiological monitoring devices in the MR environment as disclosed in:    Hu X., Le T. H., Parrish T., Erhard P., “Retrospective estimation and correction of physiological fluctuation in functional MRI”, Magn. Reson. Med., 1995, 34, 201–212;    Glover G. H., Li T. Q., Ress D., “Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR”, Magn. Reson. Med., 2000, 44, 162–167; and,    Chuang K. H., Chen J. H., “IMPACT: Image-based physiological artifacts estimation and correction techniques for functional MRI”, Magn. Reson. Med., 2001, 46, 344–353, herein incorporated by reference. The benefit of this method is that imaging sessions are not lengthened by the need for collecting extra data for phase correction.
In addition to these methods aimed at reducing physiologically-induced artifacts before images are reconstructed, methods based on image pixel intensity fluctuation have been developed for removing artifacts after images have been reconstructed as disclosed in:    Glover G. H., Li T. Q., Ress D., “Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR”, Magn. Reson. Med., 2000, 44, 162–167; and,    Chuang K. H., Chen J. H., “IMPACT: Image-based physiological artifacts estimation and correction techniques for functional MRI”, Magn. Reson. Med., 2001, 46, 344–353, herein incorporated by reference. Here, a time course of reconstructed image pixel intensity is processed using a one-dimensional Fourier transform (FT) to determine the average magnitude of the frequency components within the time course. Filtering of unwanted frequencies is achieved, for example, by multiplying the resulting frequency spectrum with a band pass filter such as a Hamming window that is unity at frequencies to be maintained and zero at unwanted frequencies. However, special care has to be taken at the data analysis stage after such filtering, since assumptions regarding temporal autocorrelations within the data are changed and, therefore, statistical tests have to be modified accordingly. The major limitation of this method is that the filter is applied over the entire time course of the experiment and not just at the time when the artifact source occurs. This is of concern in cases when the frequency content of the signal is important in data analysis. One approach to overcome this limitation is based on using Short-Time window FT (STFT). However, subtle changes in frequency are missed if the window width is insufficient in resolution. Hence, a priori knowledge of the frequency content of the signal is required.